More Schooling Is Not Always Better: Evidence from an Instrumental Variables Approach to Educational Reform in Vietnam Phu Viet Le Fulbright Economics Teaching Program August 19, 2015 1 / 41
Table of contents 1. History of education reforms in Vietnam and estimates of returns to education 2. Estimating the return to education 3. Using instrumental variables to estimate the causal effect of education on earnings in Vietnam 4. Results and robust checks 5. Conclusions and policy implications 2 / 41
History of education reforms in Vietnam and estimates of returns to education In the 20th century alone, Vietnam undertook three education reforms in 1950, 1956, and the last one in 1980s, after each major political upheaval: ◮ The 1950 education reform: reduced from 11 years under a French colonial system to 9 years, including three levels corresponding to 4, 3, 2 years. ◮ The 1956 education reform: shifted from a 9-year and 12-year system in the North and the temporarily liberated region to a 10-year system like that in the Soviet Union, with each level taking 4, 3, and 3 years. ◮ The third education reform in 1980s: changed from the 10-year system to 12-year system like that in North America which was already implemented in the South. ◮ Obstacles in assessing quality due to lack of criteria and/or data. 3 / 41
The third education reform in 1980s ◮ Context: two concurrent education systems existed in the North and the South of the 17th parallel after the unification in 1975. The North followed communist-oriented 10-year education system, while the South still kept its North American 12-year system. ◮ The third education reform unified these two systems under a newly formed general education system throughout the country which takes 12 years to complete. ◮ In the new education system, primary, lower secondary and upper secondary school takes 5, 4, and 3 years, respectively. 4 / 41
Timeline of the third education reform Breakdown of grades (left) and transition time (right). Source: VHLSS 5 / 41
Characteristics of the third education reform ◮ The third education reform included a series of small educational renovations rather than a quick adoption of a new system, unlike Doi Moi , the most dramatic economic reform in 1986. It took many years, from 1979 to 1996. Reforms on curriculum and textbooks were even much more cautious. ◮ Primary education remained free as it was aimed for universalization. However, tuitions were required for secondary schools. Increasing private contributions and semi-public schools were allowed. Irregular expenses and supplementary teaching were widespread. ◮ Lower secondary schools required 33 weeks, which was shorter than that in other comparable countries. ◮ The teaching method was teacher-centered, less interactive, which encouraged rote-learning. 6 / 41
Data sources and samples ◮ Four rounds of Vietnam Household’s Living Standard Surveys (VHLSS) in 2004, 06, 08, and 10. ◮ The focus is a group with the highest obtained education level not exceeding a high-school diploma, expectedly the most affected group by the additional schooling year than college or university graduates. This group may also have the highest return to schooling. ◮ Restricted to non-farm wage earners, who were at least 20 years old, and not currently enrolled in any school. ◮ Other important reasons for excluding those with higher educational levels. 7 / 41
Descriptive summaries of the data North of the 17th parallel Mean and standard deviation (SD) ∗ Variable 2004 2006 2008 2010 Age 35.05 35.07 35.71 35.95 SD (10.98) (11.43) (11.13) (11.24) Annual Wage (x1000) 7,840.39 10,230.59 14,512.24 22,366.23 SD (6,173.53) (7,742.54) (11,392.26) (14,105.42) EDUC 9.82 9.93 9.96 9.76 SD (2.07) (2.02) (2.05) (2.42) EXP 7.67 7.68 7.50 — SD (7.56) (7.79) (7.18) Observations 2,074 2,191 2,261 2,307 ⋆ EXP: actual years of experience at the time of survey, unlike a standard Mincer approach which assumes EXP = Age − EDUC − b . b is the age of compulsory education. Why? job switching is prevalent, and experience may be unrelated to current work. ⋆ The official exchange rate during this period increased from USD/VND 15,746 in 2004 to 18,613 in 2010. 8 / 41
Impact of years of birth on the average number of years in school ∗ 2004 2006 2008 2010 ∗ Point estimates and 95% confidence intervals, using regressions on year dummies. 9 / 41
Rate of return to an additional schooling year in various countries ∗ 10 / 41
Estimating the return to education Relying on Mincer (1974)’s study: logY i = logY 0 + β 1 × EDUC i + β 2 × EXP i + β 3 × EXP 2 i + ε i ⋆ Dependent variable: logY i is the logarithm of income. ⋆ Explanatory variables: ◮ EDUC i is the total number of years in school, could be transformed in educational degrees obtained: completing 12 years in the new education system corresponds to a high-school diploma etc. ◮ EXP i is the years of experience. ◮ May include other explanatory variables representing demographic and sectoral factors. 11 / 41
Estimating the return to education (2) logY i = logY 0 + β 1 × EDUC i + β 2 × EXP i + β 3 × EXP 2 i + ε i Meaning of the coefficients: ◮ β 1 : the average return to an extra year of schooling. For example, β 1 = 0 . 1 then each year of schooling will raise income by about 10%. ◮ Experience has a nonlinear impact on income as represented by an inverted U-shaped function ( β 2 > 0 and β 3 < 0). Experience may be more important for young workers but less important for more senior people. The marginal return to experience is calculated as { β 2 + 2 β 3 × EXP i } . The optimal number of years of experience is { EXP ∗ = − β 2 2 β 3 } , which is the peak of the experience-income parabola. This was about 26 years in a study in Eastern European countries and in Vietnam as well. 12 / 41
Problems in estimating the return to education ⋆ Two major problems: omitted variables bias, and measurement errors. ◮ Omitted variables: personal abilities affect the number of years in school and earnings. If personal abilities were not accounted for, ordinary-least-squares estimates may be biased due to correlations between the dependent variables (income) and the residuals. ◮ Measurement errors: it is difficult to measure education time due to different criteria of ”what is education?”. Reported estimates are about 10-15% less than the actual numbers (Angrist and Krueger, Card). Then, OLS estimates are also biased. ◮ Other problems including the endogeneity of education and functional forms of the return to education. 13 / 41
Omitted variables bias problem logY i = logY 0 + β 1 × EDUC i + β 2 × EXP i + β 3 × EXP 2 i + γ × Ability i + ε i ⋆ Ability represents unobserved individual characteristics such as intelligence. Then, following Griliches, 1977: E [ b ] = [ X ′ X ] − 1 X ′ Y = [ X ′ X ] − 1 X ′ [ X β + γ × Ability + ε ] = [ X ′ X ] − 1 X ′ X β + γ [ X ′ X ] − 1 X ′ Ability + [ X ′ X ] − 1 X ′ ε � �� � � �� � � �� � = β =0 = γ Cov [ Ability , EDUC ] Var [ EDUC ] ⇒ E [ b 1 ] = β 1 + γ Cov [ Ability , EDU ] Var [ EDUC ] ⋆ Due to an expected positive correlation between individual ability and education (barring some special cases such as Bill Gates or Steve Job!), the estimated return to education may be exaggerated (thus suffered from an upward bias). 14 / 41
Measurement errors problem ⋆ Incorrect measures of education may also skew the least-square estimates. In this case, we might estimate a return to EDUC ∗ instead of the actual level of education EDUC: EDUC = EDUC ∗ + errors logY i = logY 0 + β 1 × EDUC i + β 2 × EXP i + β 3 × EXP 2 i + ε i − β 1 × errors i � �� � composite residuals ⋆ Violation of a Gauss-Markov assumption on no correlation between the explanatory variable EDUC and the residual: Cov [ EDUC ∗ + errors , ε − β 1 × errors ] = β 1 σ 2 e � = 0 ⇒ biased coefficient. ⋆ In the presence of measurement errors, the estimator of the return to education is: Var [ EDUC ] E [ b 1 ] = β 1 Var [ EDUC ] + Var [ errors ] ⇒ Downward bias (attenuation bias) of the actual value. Magnitude depends on the signal-to-noise ratio. 15 / 41
Functional form of the return to education: linear or nonlinear? logY i = logY 0 + β 1 × EDUC i + β 2 × EXP i + β 3 × EXP 2 i + ε i ⋆ If the above function is correct (and conditions for least-square estimation hold) then the return to education β 1 is BLUE. ⋆ Some studies used a second order polynomial of education β 1 × EDUC i + β 2 × EDUC 2 i in the same way as the experience variable. ⋆ How to know which functional form is correct? ⇒ Non-parametric local regressions may help, without any condition on the functional form. 16 / 41
Using nonparametric local regression to approximate the return to education ◮ First stage, filter out residual variations ˜ Y due to other factors not education from the earning equation: i + ˜ logY i = logY 0 + β 1 × EDUC i + β 2 × EXP i + β 3 × EXP 2 Y i Then, ˜ Y supposedly contains only variations due to education (and other uncontrolled factors). ◮ Second stage, estimate the residual earning function on the education level: ˜ Y i = g ( EDUC i ) + ε i g(.) is a polynomial of arbitrary order. ◮ Due to a nonparametric nature, we use graphical representation of the estimated result. This method is also called LOESS or LOWESS (locally weighted scatter-plot smoothing). 17 / 41
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